Proceedings Article10.1109/CIC.1988.72580
Improving left ventricular border recognition using probability surfaces
R.E. van Bree,David L. Pope,Dennis L. Parker +2 more
- 25 Sep 1988
- pp 121-124
TL;DR: This method uses a large clinical database of LV borders to generate a probability surface of expected border locations and automates the generation of search targets used in finding borders of the left ventricle (LV) from digital-subtraction angiography images and thus further automating LV-border determination.
read more
Abstract: The authors present a method for automating the generation of search targets used in finding borders of the left ventricle (LV) from digital-subtraction angiography (DSA) images and thus further automating LV-border determination. This method uses a large clinical database of LV borders to generate a probability surface of expected border locations. A composite image of all observed border points is used to create a set of extraction lines that is overlaid on the LV of interest. Border points are found by using a dynamic search on the density matrix formed by extracting pixel values at the points defined by the extraction matrix. User interaction is only needed to define the valve plane, confirm the final border, and make corrections if needed. LV contours then be used to find end-diastole and end-systole volume and ejection fraction. >
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Patent
Method for determining the contour of an in vivo organ using multiple image frames of the organ
Florence H. Sheehan,Robert M. Haralick,Chang-Kyu Lee +2 more
- 31 May 1994
TL;DR: In this article, a method for automatically evaluating image data taking over a sequence of image frames to determine a contour of a left ventricle (LV) was proposed, where the image frames are converted to digital data that identify a gray scale value for each pixel in each image frame.
169
Epicardial boundary detection using fuzzy reasoning
Jie Feng,W.-C. Lin,Chin-Tu Chen +2 more
TL;DR: A fully automated system for detecting the endocardial and epicardial boundaries in a two-dimensional echocardiography by using fuzzy reasoning techniques is proposed, which deduces local intensity change from the knowledge of global intensity change through fuzzy reasoning.
102
Extraction of left ventricular contours from left ventriculograms by means of a neural edge detector
TL;DR: The proposed contour-extraction method was able to extract the contours in agreement with those traced by an experienced cardiologist, and achieved an average contour error of 6.2% for left ventriculograms at end-diastole.
99
Greedy Algorithm for Error Correction in Automatically Produced Boundaries from Low Contrast Ventriculograms
TL;DR: Two calibration methods are discussed: the identical coefficient and the independent coefficient to remove systematic biases and a fused algorithm is constituted which reduces the boundary error compared to either of the calibration methods.
91
Left ventricular boundary detection from spatio-temporal volumetric computed tomography images
TL;DR: The goals of this paper are to incorporate the temporal information intoLV boundary detection, to link the shape modeling and LV boundary detection together, and to provide a compact representation of recovered LV boundaries to cardiac imaging.
26
References
Variability in the measurement of regional left ventricular wall motion from contrast angiograms.
TL;DR: Investigators whose methods of wall motion analysis rely on identification of the apex as a landmark should be aware of this source of potential variability and error.
167
•Journal Article
Variability in the measurement of regional left ventricular wall motion from contrast angiograms.
TL;DR: In this article, four types of variability affecting quantification of regional wall motion from contrast left ventriculograms (LVgrams) were studied, i.e., beat-to-beat, intraobserver, interobserver and inter-observer variability.
81
Left ventricular border recognition using a dynamic search algorithm
TL;DR: The modular dynamic search algorithm is shown to perform better than previously described algorithms, which generally require operator interaction, and it is shown that for both manual and automated techniques, ventricular bordersderived from subtracted images may be significantly different from borders derived from nonsubtracted images.
63
Determination of left ventricular contours: a probabilistic algorithm derived from angiographic images.
TL;DR: A probabilistic algorithm for automated left ventricular contour detection is developed which uses information extracted from a variety of angiographic images, and shows over 90% of computer-determined coordinates to lie within the interval of reproducibility for manually traced contours.
26